We propose a novel modeling framework for characterizing the time course of change detection based on information held in visual short-term memory (VSTM). Specifically, we seek to answer whether change detection is better captured by a first-order integration model, in which information is pooled from each location, or a second-order integration model, in which each location is processed independently. We diagnose whether change detection across locations proceeds in serial or parallel and how processing is affected by the stopping rule (i.e., detecting any change vs. detecting all changes; Experiment 1) and how the efficiency of detection is affected by the number of changes in the display (Experiment 2). We find that although capacity is generally limited in both tasks, the architecture varies from parallel self-terminating in the OR task to serial self-terminating in the AND task. Our novel framework allows model comparisons across a large set of models ruling out several competing explanations of change detection. (PsycInfo Database Record (c) 2021 APA, all rights reserved)

中文翻译：

---

表征变化检测中决策的时间过程。

我们提出了一种新颖的建模框架，用于基于视觉短期记忆 (VSTM) 中保存的信息来表征变化检测的时间过程。具体来说，我们试图回答变化检测是否更好地被一阶集成模型（其中信息从每个位置汇集）或二阶集成模型（其中每个位置被独立处理）捕获。我们诊断跨位置的变化检测是串行还是并行进行，以及处理如何受到停止规则的影响（即检测任何变化与检测所有变化；实验 1）以及检测效率如何受到变化数量的影响。显示（实验 2）。我们发现，虽然这两项任务的能力通常是有限的，架构从 OR 任务中的并行自终止到 AND 任务中的串行自终止。我们的新颖框架允许跨大量模型进行模型比较，从而排除了变化检测的几种相互竞争的解释。（PsycInfo 数据库记录 (c) 2021 APA，保留所有权利）